Bayesian estimation of the global minimum variance portfolio
(2017) In European Journal of Operational Research 256(1). p.292-307- Abstract
In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we... (More)
In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we analyze the posterior densities of the weights of an international portfolio.
(Less)
- author
- Bodnar, Taras ; Mazur, Stepan LU and Okhrin, Yarema
- organization
- publishing date
- 2017-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Credible interval, Global minimum variance portfolio, Posterior distribution, Wishart distribution
- in
- European Journal of Operational Research
- volume
- 256
- issue
- 1
- pages
- 16 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:84990243844
- wos:000384854500027
- ISSN
- 0377-2217
- DOI
- 10.1016/j.ejor.2016.05.044
- language
- English
- LU publication?
- yes
- id
- 41aa578c-2044-42b0-a80b-816517409cc1
- date added to LUP
- 2016-10-19 09:30:10
- date last changed
- 2024-06-14 15:56:00
@article{41aa578c-2044-42b0-a80b-816517409cc1,
abstract = {{<p>In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we analyze the posterior densities of the weights of an international portfolio.</p>}},
author = {{Bodnar, Taras and Mazur, Stepan and Okhrin, Yarema}},
issn = {{0377-2217}},
keywords = {{Credible interval; Global minimum variance portfolio; Posterior distribution; Wishart distribution}},
language = {{eng}},
month = {{01}},
number = {{1}},
pages = {{292--307}},
publisher = {{Elsevier}},
series = {{European Journal of Operational Research}},
title = {{Bayesian estimation of the global minimum variance portfolio}},
url = {{http://dx.doi.org/10.1016/j.ejor.2016.05.044}},
doi = {{10.1016/j.ejor.2016.05.044}},
volume = {{256}},
year = {{2017}},
}